COVER SHEET

COVER SHEET
Campbell, A.B. and Pham, B. and Tian, Y.-C. (2005) Delay-Embedding Approach to
Multi-Agent System Construction and Calibration. In Zerger, A. and Argent, R.M.,
Eds. Proceedings MODSIM 2005 International Congress on Modelling and
Simulation. Modelling and Simulation Society of Australia and New Zealand, pages
pp. 113-119, University of Melbourne, Australia
Copyright 2005 Modelling and Simulation Society of Australia and
New Zealand
Accessed from: http://mssanz.org.au/modsim05/papers/campbell.pdf
A Delay-Embedding Approach
to Multi-Agent System Construction and Calibration
Campbell, Alex and Pham, Binh and Tian, Yu-Chu
Computational Intelligence Group, Faculty of Information Technology
Queensland University of Technology, GPO Box 2434, Brisbane Qld 4001, Australia. E-Mail: ab.campbell@qut.edu.au
Keywords: Delay-Embedding, Simulation, Calibration
EXTENDED ABSTRACT
fashion, while still retaining nonlinear relationships.
Agent-based modelling offers a way to break from the
crude assumptions of mean-field type models, which
ignore space correlations between elements of the
system and replace local interactions with uniform
long-range ones.
Multi-Agent Systems (MAS)
explicitly model spatially distributed individuals;
however the richness of such a model can also be
a liability due to the sensitive dependence of such
high-dimensional systems. This has implications for
choice of MAS architecture, programming of rules,
confidence in predictions, and calibration of model
parameters.
Cluster-Weighted Modelling is a sophisticated approach to density estimation that, when applied to
the output of a delay-embedding process, is able to
obtain a statistical representation of the dynamics.
These two concepts - forced embeddings and density
estimation - provide a promising theory and a practical
probabilistic interpretation respectively to the ‘inverse
problem’ of system identification.
Delay-embedding, also known as geometry from a
time series, provides a deep theoretical foundation
for the analysis of time series generated by nonlinear
deterministic dynamical systems.
The profound
insight of embedding is that an accessible variable can
explicitly retrieve unseen internal degrees of freedom.
[3].
In the domain of complex systems modelling,
however, there typically exist an abundance of
observables, in which case reconstructing hidden
degrees of freedom may be problematic or even
nonsensical. Also, many observables often implies
high dimensionality, which generally precludes a
dynamical systems approach in the first instance. Uncautious use of delay-embedding, from which it is
easy to get a result regardless of physical justification,
has in the past led to a degree of negative press for this
idea.
However, the recent extensions of Takens’ delayembedding theorem to deterministically and stochastically forced systems [9, 8, 10] provide a rigorous
framework in which to reconstruct using multiple
observables.
This holds great significance for
pattern discovery in complex data series, which
we define to be more than one series - spatial,
temporal or a mixture - of an underlying complex
system. In particular, the concept of a bundle
embedding highlights a way to usefully employ the
‘surplus’ observables in the embedding process. More
generally, forced embeddings provide a methodology
to breakdown complex system data sets in a modular
113
Expert-knowledge based MAS construction and
density-estimation of delay-embedded data can
therefore be thought of as two complementary
approaches to the goal of bottom-up, complex systems
modelling. The original contribution of this paper is to
present the latter as a highly data-driven approach to
MAS construction in its own right, and, perhaps more
importantly, as an aid to constructing and calibrating
the more expert-knowledge rule-driven approach. The
emphasis is on a solid theoretical and conceptual
foundation.
To illustrate the feasibility of our approach, preliminary implementation results for an ecological
modelling scenario are presented and discussed.
1.
INTRODUCTION
The analysis of time series data has benefited greatly
from the concept of delay-embedding, also known
as geometry from a time series after the paper in
which it was introduced by Packard et al. in 1980
[6]. Under reasonable technical conditions this allows
the reconstruction of a multi-dimensional phase space
from a series of a single observable only [11].
Recent progress in extending delay-embedding theory
to ‘forced’ systems (perturbed or driven by some
external influence) provides a platform for more
general application of this principle. One significant
result coming from this work is a sound justification
for modelling high-dimensional spatially extended
systems as a collection of subsystems at different
spatial locations coupled together, in a sense driven
by the ‘noise’ of the neighbouring systems.
Expert-knowledge based MAS construction and
density-estimation of delay-embedded data can be
thought of as two different approaches to the goal of
bottom-up, complex systems modelling. Furthermore,
they are complementary in a number of specific ways.
For example, one problem for the application of
delay-embedding is that generally the training data
requirements are exponential in the dimensionality of
the dynamics. In a MAS the data is generated by a
simulation, it is therefore possible to analyse higher
dimensional data.
Delay-embedding, forced delay-embedding, and
cluster-weighted modelling are introduced and integrated into a single spatial-temporal systems
identification approach in Section 2. A conceptual
framework for multi-agent system construction and
calibration is presented in Section 3. We apply this to
an ecological scenario with some real data in Section
4. In Section 5 we conclude and discuss future work .
2.
DELAY-EMBEDDING AND CWM
2.1.
in its ‘image’, Φ(xt ), that each point reached has
a unique starting point in xt , and that it (the map)
is differentiable in both directions. Essentially this
imposes a certain degree of equivalence between the
spaces. The term ‘almost every’ captures what is
meant by generic in the field of dynamical systems,
and is defined formally, but in practice this turns out
not to be a major obstacle. Because d ≥ 2m + 1 is
a sufficient condition, an embedding may be possible
for d < m ≤ 2m.
The consequences of this are far-reaching. We can
define the map,
F = Φ ◦ f ◦ Φ−1 ,
which can be seen as the same dynamical system as
F under the coordinate change given by Φ. Because
of the equivalence mentioned above, coordinateindependent quantities on F and f such as Lyapunov
exponents and correlation dimension (and many
others) will be identical.
Given a smooth observation function h : M → R, and
setting ot = h(xt ),we define an instance of Φ as
Φf,h,τ (xt ) = (h(xt ), h(f −τ (xt ), ..., h(f −(d−1)τ (xt ))
= (ot , ot−τ , ..., ot−(d−1)τ )
(2)
This provides a snapshot of the system zt =
Φf,h,τ (xt ) in this new space, Rd , the dynamics of
which are given by F . Remarkably, F is defined
entirely in terms of a τ -shift in the observations,
F τ (ot , ot−τ , ..., ot−(d−1)τ ) = (ot+τ , ot , ..., ot−(d−2)τ )
This means that we can deduce many of the properties
of F , and hence of f , purely in terms of the observed
time series, and despite not knowing any of the
dynamics a priori. From a forecasting perspective this
is also very strong result: the next state has only one
unknown, ot+τ , which is a function of the d previous
values,
Standard Delay-Embedding
ot+τ = G(ot , ot−τ , ..., ot−(d−1)τ ),
Assume that at each point in time a system is
determined completely by a point x lying in a
m-dimensional phase space, which we denote M .
Technically M needs to be a manifold1 , and is often
some subset of Rn . The time evolution of such a
system (the movement of the point through phase
space) is given by a map f τ : M → M such that
xt+τ = f τ (xt ).
Takens’ theorem [11] says that ‘almost every’ smooth
map Φ : M → Rd , d ≥ 2m + 1, is an embedding.
This means that the map Φ can get to every point
1A
(1)
manifold is a space that locally looks like a ball in Rn .
114
(3)
and can be estimated from training data to forecast
future values the time series.
The coordinate
transform is illustrated in Figure. 1.
To make practical use of delay-embedding, suitable
values for the delay τ , and the embedding dimension
d, need to be identified. There is a large body
of literature on this. The first minimum of the
mutual information is popular for finding a suitable
delay [2], and the embedding dimension can be
estimated via the correlation dimension or a multidimensional extension of mutual information called
the redundancy [3].
issue.
M
xt+τ
xt
Φ−1
This is a very interesting result, implying that even
if we have only one observable it is possible to
reconstruct dynamics of the whole (expanded) phase
space. But the dimensionality of the reconstructed
phase space is necessarily increased by the dimension
of N , which is a problem for complex data, and it
does not usefully employ knowledge of more than one
observable.
Φ(M )
zt+τ
zt
Rn
Φ
Rd
Rather than requiring Φf,g,h to embed M × N , Stark
et al. asked what if we simply require that we can
embed each M × {y}. If knowledge of yi = g i (y) is
available, then
Figure 1. The coordinate transform Φ
2.2.
Forced Delay-Embedding
The theory outlined above is a remarkable result, however the assumptions that the system is deterministic
(it has no random aspect) and autonomous (it is not
‘driven’ or ‘forced’ by any external events) are not
justified for many real world applications. In fact real
world systems are almost always forced by something:
even if you are considering the whole planet to be
your dynamical system, there would still be external
forcing from, for example, solar activity and meteors.
There have since been a number of generalisations
of the theory, most significantly a method for
reconstructing input-output systems conjectured by
Casdagli [1] and subsequently proved by Stark et
al. [9]. Building on this, Stark and colleagues
give an thorough presentation of a number of forced
embedding theorems in [8] and [10], covering both
deterministic and stochastic forcing. These extensions
dramatically increase the degree to which real world
data can be considered from a formal basis. The
forced theorems are given for the discrete time case,
so we will use i ∈ Z rather than t ∈ R and, f i is just
f composed i times.
A standard approach to modelling a non-autonomous
system in dynamical systems theory is to replace
it with a new, autonomous system, constructed by
including the driving dynamics. In other words we
are expanding the phase space to include the driving.
A standard model is
xi+1 = f (xi , yi )
yi+1 = g(yi ),
(4)
where the pair f : M × N → M and g : N → N is
known as a skew product on M × N .
Stark et al. prove that the map Φf,g,h is generically an
embedding, provided some conditions on the periodic
orbits of the forcing are satisfied. This requirement
may cause problems in an experimental situation
where the sampling is done commensurate with the
forcing, but in a real-world data situation it is not an
115
zi = (o(xi ), o(f −1 (xi , yi )), ..., o(f −(m+1) (xi , yi )))
= Φf,g,h (xi , yi )
This is what Stark et al. call a bundle embedding.
The name ‘bundle’ arises from a technical concept
in topology which do not go into details of, but
essentially it means that it is possible to just
reconstruct M , as long as each embedded point is
indexed by the value of the forcing at that time. This
means that the map
oi+1 = Gyi (oi , oi−1 , ..., oi−d+1 )
(5)
is identical to the forecasting map of the standard
delay-embedding theorem, except for an index of
the forcing. By using our additional data it is then
possible to estimate Gyi as a function from Rd × N .
Most significantly, the required dimensionality of the
reconstructed phase space remains >= 2m + 1.
One final problem with this model is the assumption
that the forcing is deterministic and that g : N → N
is known. For real world data the types of forcing that
we want to consider are best modelled as stochastic
variables. For details on the proof of this and the
other embedding theorems we refer the reader to [8]
and [10]. Practically speaking, a powerful way to
deal with (and learn) probability densities is provided
by the machine learning technique Cluster-Weighted
Modelling.
2.3.
Cluster-Weighted Modelling
Despite the theoretical implications of delay embedding - that it is possible to handle highly nonlinear behaviour of arbitrary physical systems with
hidden dynamics - it is still very difficult to deal
with the errors and uncertainty in real data. This is
where a probabilistic approach is essential. ClusterWeighted Modelling is a sophisticated approach to
learning probability density functions in the presence
of nonlinear, and even nonstationary dynamics. The
framework is based on density estimation around
Gaussian kernels which contain simple local models
describing the system dynamics of a data subspace
[3].
The essential idea underlying the CWM procedure
is to approximate an unknown model, M, of the
dynamical system by a set of local nonlinear models
denoted Ck , k = 1, 2, ..., K. The model, M, outputs
the time series y given input data x, and is constructed
as a Gaussian mixture over a suitably chosen set {C}.
Each local model Ck is obtained via fitting a nonlinear
function, namely, by obtaining a set of parameters βk
in suitably chosen nonlinear maps
y = f (x, βk )
=
X
(7)
p(y|x)p(x|cm )p(cm )
1≤m≤M
The probability of a given local model
PK is denoted
p(Ck ), with the usual normalization k=1 p(Ck ) = 1.
Denote by Px,k and Py,k the covariance matrices for
the input and output data, and by Dx and Dy the input
and output dimensions. A useful choice for the input
distribution p(x|Ck ) is a Gaussian density,
p(x|ck ) =
|Px,k |1/2 −(x−µk )T Px,k (x−µk )/2
e
(2π)Dx /2
(8)
where µk are the cluster means. Since the input and
output data are related via Eq. 6, this gives an output
distribution of the form
T
Py,k
e−[y−f (x,βk )] Py,k [y−f (x,βk )]/2
D
/2
(2π) y
(9)
The posterior probability, which can be computed
through Eqs. 6,8, and 9 as
p(y|x, Ck ) =
p(y, x|Ck )p(Ck )
p(Ck |y, x) = PK
,
j=1 p(y, x|Cj )p(Cj )
2.4.
Spatially Extended Systems
(6)
For generality, the maps f are taken to be polynomial
functions. The joint probability distribution p(y, x)
is expressed as a sum over the densities coming from
each local model, — where µk are the cluster means.
Since the input and output data are related via Eq. 6,
this gives an output distribution of the form
X
p(y, x) =
p(x, y, cm )
1≤m≤M
easy to interpret, and by definition the clusters find
‘interesting’ places to represent the data. Another is its
flexibility: it is possible to include more than one type
of local model and let the clustering find where they
are most relevant. This has particular relevance for
hypothesis testing, as we will see below. Also, it can
model nonstationary data sets by including absolute
time as another input: the clusters will then spread
out to describe different periods in time. This concept
extends to space as well, again, this is particularly
relevant for our purposes.
(10)
is maximized with respect to model parameters βk
which can be recursively estimated by an iterative
search procedure, the expectation-maximization algorithm. For further details (especially regarding
how this provides a measure of the conditional
uncertainty), see [3].
Perhaps the most important advantage CWM brings
is a transparent architecture: the parameters are
116
The basic idea, presented in [4], is that a spatially
extended system can be modelled as a number of
local spatial subsystems weakly coupled to, and
driven by, the ‘noise’ at their boundaries. In other
words M is a local spatial region, and it forms the
part of the whole system from which we can take
observables. Therefore our delays may also have
a spatial component - this corresponds to recording
values of neighbours. The forcing by the rest of
the lattice is modelled similar to the standard forced
system above, except that the update of the forcing
is now itself dependent on x. This makes intuitive
sense: if the point in M is updated, the forcing from
the lattice must be updated, because M is part of the
lattice. This means that it is no longer possible to
have an embedding in the rigorous, technical sense
of the word. However, because the effect M has
on the forcing dynamics is generally small, Orstavik
and Stark felt that the delay embedding technique still
provides the correct intuition. This is supported by
experimental results in their paper, and in a number
of other studies before and since, many of which
have found that the reconstruction technique to work
suprisingly well. Interestingly, it is delays in both
space and time (rather than just time or just space) for
which the best results were found. The forced delayembedding theorems of the previous sections began
as an outline of a generalisation of Takens’ theorem to
account for this and apply to systems which are only
‘approximately’ low dimensional.
The consequences of all this is a unified framework
for dealing with arbitrary forced dynamical systems,
that has a truly holistic treatment of space and time.
To see this, consider an example prediction map G:
r+1
h(xri ) = Gxi ,ωi (h(xri−1 ), h(xr−1
i−1 ), h(xi−1 )) (11)
where we are using superscripts to denote spatial
delay. The function we would like to learn, G, treats
spatial delays and temporal delays indiscriminately
(though of course it does not have to, practically
speaking weighting the number of delays and their
distance differently for space vs time may make more
sense ).
In the next section we show how these features forced ‘bundle’ delay embeddings, cluster-weighted
modelling, and spatial forcing - can be combined to
provide a prescription for constructing a MAS.
3.
similar probabilistic behaviour. This is useful
because the PR rules may be much easier to
interpret.
2. As a complementary approach to PR MAS
construction when there is limited and/or poor
quality data. In this guise it is designed to
be used in parallel with PR modelling in an
iterative fashion. See Figure 2
EMBEDDINGS FOR MULTI-AGENT SYSTEM CONSTRUCTION AND CALIBRATION
The prescription for MAS construction by DelayEmbedding and Cluster-Weighted Modelling (the
DECWM approach) is as follows. Using CWM
(or some other form of function approximation
technique), and given a sufficient amount of low noise
training data, it is possible to learn the mapping G
given above (even if only for a region of its inputoutput space). This is done initially for one variable,
which we call the ‘target’ variable. For example
this may be house-prices on a grid in an urban
economic modelling scenario. Once learned, this
will then act as a ‘rule’ of a cellular automata type
model. Following the bundle embedding concept, we
extend G to include other ‘explanatory’ variables as
forcings, for example spatially explicit demographic
data. For each additional dimension induced by
the spatial or temporal delays, the forcing must be
considered. However, crucially this does not increase
the dimensionality of the space into which we are
embedding. The dimensionality of the dynamics
and the delay magnitude must be determined by
the methods mentioned earlier, for example the
mutual information and correlation dimension. Lower
dimensionality indicates greater ability to make
predictions.
The novel aspect to the use of the bundle embedding
concept is that the more data that is ‘explanatory’ of
the target variable is available, the less dynamics has
to be explained through target variable itself, and the
lower the dimensionality.
Generally speaking this will be inadequate as a standalone approach for real world data. Firstly it just
provides a model for one spatiotemporal variable.
It therefore must be trained again on other ‘target’
variables to build models for them (although the
possibility of incrementally synthesising ‘coherent’
models by doing so is an interesting prospect we
intend to focus on in future work). Secondly it
obviously is limited by the availability and quality
of data. However, the combination of this with the
conventional Programmed Rules (PR) approach can
be useful in three distinct ways:
1. As a validation tool. If there is high enough
quality data to get decent predictions on real
world data then the DECWM method can be
used to validate a PR model by training it on
the output of a PR model and running it to show
117
3. As an analysis tool for ‘artificial’ MAS, for
which data is not available, or is not necessary.
Here the link between the PR and DECWM
approach is closest.
Both approaches have their strengths and weaknesses:
consciously programmed rules are of course easier to
understand and interpret, however inference from data
often is more accurate in terms of predictions if there
is sufficient good quality data available.
Real World
Measurements
E.g. Remote
Sensing
Expert Knowledge
PR MAS
STTS
STTS
Mesoscopic Behaviour, Hypothesis Validation
DECWM
Compare, Revise Hypotheses
(DECWM) MAS
Predictions, Conditional Uncertainty
Figure 2.
4.
AN EXAMPLE
We consider a continuous version of a cellular
automata model known as a coupled map lattice.
Although cellular automata are highly simplified
compared to most MAS, they do retain the essential
elements. They are a prototypical form of MAS, and
therefore well suited to this preliminary study.2
Data for the ecological scenario we consider comes
from a study on wading birds in North America. A
particular data set was first considered by Ozesmi [5]
in an ecological modelling context, and then taken
up as a spatial data mining problem by Shekhar
et. al. [7]. It consists of three explanatory
variables: “distance to open water”, “water depth”,
and “vegetation durability”, though we only use the
distance to open water variable in this study.
2 More images and implementation details can be accessed at
http://sky.fit.qut.edu.au/‘campbeab/decwm.html
MAS Simulation The MAS we simulate to generate
our space-time data is based on the nonlinear H´enon
map:
xri+1 = 1 − α((1 − µ)xri + µ
N (xri ) 2
) + βyir (12)
4
where x and y correspond to a two-dimensional
target system at site r and time step i. N (xi ) is a
neighbourhood function, returning the value of x at the
four sites adjacent to r. µ is the spatial coupling, and
for model parameters α = 1.45 and β = 0.3 we are in
the chaotic region. The target system is mildly forced
by the “distance to open water” variable through the
beta parameter - i.e. the parameter beta will vary
across the lattice. A snapshot of the simulation is
given in Figure 3.
Figure 3. Snapshot of simulation. State variables
and landscape variables represented by red-green-blue
level of each cell. The ‘agent’ variables are red and
blue, the distance to open water is green.
Delay-Embedding. In Figure 4, the observed
variable is being measured with only one delay in
time, i.e. we are synthesising a two-dimensional
system. Any two of the axes represent these two
degrees of freedom, with the third recording the
value of the one dimensional time series at the
next time-step. In fact this redundancy was just
done to give us training data - you can see that
the third dimension is not really required here.
Experiments with spatial delays are being carried out
- we do not have any visualisations for them as yet,
but we still make some comments in this regard.
Because the dynamics are translationally invariant
it is not necessary for one spatial delay to give us
four additional dimensions. For more complicated
dynamics that are still rotationally invariant (an
assumption which is valid for many systems) we
suggest using a form of averaging or even principal
component analysis on the spatial delays. Technically
the time and space dimensions should multiply to
give us a four-dimensional embedding space, however
in many cases an approximation of three (the more
‘distant’ space-time dimension is not used) seems to
give comparable results.
118
Figure 4. Delay Embedding
Cluster-Weighted
Modelling. Cluster-weighted
modelling of the delayed variables using two clusters
is shown in Figure 5. As mentioned, it is possible
Figure 5. CWM with two clusters
to include absolute space as an input to the model;
clusters will then spread out to describe different
spatial areas. This combines nicely with the ability
to program different functional relationships between
inputs and outputs for the different clusters: the
cluster or clusters with that feature should move to
that spatial region. This provides a good (perhaps
rather challenging) test for that knowledge/intuition.
For example in the current scenario we may believe
that a special relationship exists along the boundaries
between the open water and the edge of the marshland,
and that this particular spatial region (even though it
is a distributed spatial region) can be described very
simply, though very nonlinearly. An example would
be that the probability for the existence of the agent
is high only if the three or more of the surrounding
sites are not open water, else it is low. In these
preliminary stages, we only are interested in showing
qualitative results. Nevertheless, it is encouraging
that good predictions are being obtained. In Figure 6,
the original data in our extended embedding space is
overlaid with the ‘prediction surface’ obtained by the
CWM procedure. Note the closeness of the surface to
the data points, and the nonlinearity afforded by the
use of two clusters.
6.
ACKNOWLEDGEMENTS
This project is supported by an Australian Research
Council Linkage grant in collaboration with the Built
Environment Research Unit (BERU), Queensland
Department of Public Works. Thanks also to Alex
Streit for Graphics API expertise.
7.
MJ Casdagli. A dynamical systems approach to modeling input-output systems. In M.J. Casdagli and
S.G. Eubank, editors, Nonlinear Modeling and
Forecasting. Addison-Wesley, Santa-Fe Institute,
1992.
Figure 6. Prediction Surface
Implementation The iterative, interactive nature of
the construction process, and the fact that it is based
on an inherently parallel model (cellular automata),
means that significant computational resources are
required. Additionally, the approximation we have
made here that the target variable (the ‘agent’) is
continuous valued most probably has to be relaxed
with the use of a Monte-Carlo approach. With this in
mind, programmable graphics hardware has been used
to accelerate the computations. Graphics hardware is
well suited to the complex systems paradigm as many
concepts and models therein are based on the parallel
execution of many local models; this is more or less
a summary of the architecture of such hardware. It is
also a good visualisation tool, which becomes useful
when contemplating three-D (and higher) embedding
spaces.
5.
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CONCLUSION AND FUTURE WORK
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